What is function calling in AI Agents? A Guide for developers in payments

Function calling is a pattern where an AI model decides when to invoke a specific software function instead of only generating text. In AI agents, it lets the model request structured actions like get_payment_status, validate_bank_account, or initiate_refund so the system can do real work.

How It Works

Think of function calling like a payment operations desk with a strict runbook.

A customer asks, “Did my card payment go through?” The agent does not guess. It checks whether it needs data, picks the right tool, passes structured arguments, gets a result back, and then responds in plain language.

The flow is usually:

• •User asks something
• •The model inspects the request
• •The system exposes a set of allowed functions
• •The model chooses one function and fills in arguments
• •Your backend executes the function
• •The result is returned to the model
• •The model formats the final answer for the user

For payments teams, this matters because the AI should not be making up transaction states. It should call your actual systems of record: payment gateway, ledger, risk engine, KYC service, or dispute API.

A simple mental model:

The key detail is that the model does not directly move money. It only decides which action to request. Your application remains in control of validation, authorization, idempotency, and audit logging.

A typical function definition might look like this:

{
  "name": "get_payment_status",
  "description": "Fetches the current status of a payment by reference ID",
  "parameters": {
    "type": "object",
    "properties": {
      "payment_reference": {
        "type": "string"
      }
    },
    "required": ["payment_reference"]
  }
}

If a user says, “Check payment PAY-88231,” the model may return:

{
  "function_name": "get_payment_status",
  "arguments": {
    "payment_reference": "PAY-88231"
  }
}

Your service runs that function against your payments backend and returns something like:

{
  "status": "settled",
  "settled_at": "2026-04-21T10:14:00Z",
  "amount": 149.99,
  "currency": "USD"
}

Then the model turns that into a user-friendly response: “Payment PAY-88231 settled at 10:14 UTC for $149.99.”

Why It Matters

If you build payments products, function calling solves real operational problems:

• •

  Reduces hallucinations

  The agent stops inventing transaction statuses, refund outcomes, or compliance answers. It retrieves facts from your systems instead.

• •

  Keeps sensitive actions controlled

  You decide which functions exist and what they can do. That means no free-form “send money” behavior without policy checks.

• •

  Improves support automation

  Agents can handle common requests like balance checks, chargeback status, payout ETA, or failed transfer reasons without escalating every case.

• •

  Fits audit and compliance needs

  Every tool call can be logged with inputs, outputs, timestamps, and user context. That matters for PCI-adjacent workflows and dispute review.

For engineering teams, the bigger win is separation of concerns. The model handles intent recognition and language generation. Your code handles business rules.

Real Example

Imagine a card issuer support agent handling this message:

“My refund hasn’t shown up yet. Can you check?”

A production setup might expose these functions:

• •lookup_transaction
• •check_refund_status
• •get_cardholder_profile
• •create_support_case

The agent first calls lookup_transaction using the last four digits of the card plus order reference from the conversation context. If it finds an original purchase but no completed refund yet, it calls check_refund_status.

Example backend flow:

def check_refund_status(refund_id: str) -> dict:
    refund = refunds_api.get(refund_id)

    if refund["state"] == "pending":
        return {
            "state": "pending",
            "message": "Refund initiated but not yet posted by acquiring bank.",
            "expected_window_days": 5
        }

    if refund["state"] == "completed":
        return {
            "state": "completed",
            "posted_at": refund["posted_at"],
            "amount": refund["amount"]
        }

    return {
        "state": refund["state"],
        "message": refund.get("reason", "Unknown state")
    }

The agent receives structured output like:

{
  "state": "pending",
  "message": "Refund initiated but not yet posted by acquiring bank.",
  "expected_window_days": 5
}

Then it responds:

“Your refund was initiated successfully and is still pending settlement with the acquiring bank. It usually takes up to 5 business days to appear on your statement.”

That’s useful because it gives support accurate status without exposing raw internal codes to customers.

This same pattern works in insurance too. An agent can call verify_policy_active, fetch_claim_status, or calculate_premium_quote, while keeping underwriting rules inside your services.

Related Concepts

• •

  Tool use

  Broader term for models invoking external APIs, databases, search tools, or internal services.

• •

  Structured outputs

  Getting JSON-shaped responses from models so downstream systems can parse them reliably.

• •

  Agent orchestration

  Managing multi-step flows where an agent plans actions across several tools before answering.

• •

  Idempotency

  Critical in payments when a tool call might be retried; you need to avoid duplicate charges or refunds.

• •

  Guardrails

  Validation layers that restrict which functions can be called and under what conditions.

If you’re building payments infrastructure, treat function calling as controlled automation. The model becomes a dispatcher, not a decision-maker for money movement.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.